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Abstract

We present a new segmentation method for multispectral remote sensing imagery using the K-means clustering aggregation coupled with local combination histogram (LCH). First of all, the bands are partitioned into several nearly uncorrelated subsets. Then, some band combinations of the multispectral images are generated from the subsets. After that, the LCHs are computed from quantized images in each band combination. The LCH represents a pixel in the sense of both spectral information and neighborhood spatial information implicitly and serves as the input feature. Identical K-means procedures are employed to get several relatively coarse segmentation maps. The final K-means procedure refines these intermediate segmentation maps to achieve the final results. The segmentation results are evaluated and compared with other multispectral image segmentation methods by visual inspection and object-based image classification. Experimental results show that the proposed method can achieve more accurate segmentation maps and higher classification accuracy.

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Journal of Applied Remote SensingJournal of Astronomical Telescopes Instruments and SystemsJournal of Biomedical OpticsJournal of Electronic ImagingJournal of Medical ImagingJournal of Micro/Nanolithography, MEMS, and MOEMSJournal of NanophotonicsJournal of Photonics for EnergyNeurophotonicsOptical EngineeringSPIE Reviews